近地轨道
卫星
人工神经网络
计算机科学
轨道(动力学)
遥感
人工智能
地质学
航空航天工程
天文
物理
工程类
作者
Haitao Yang,Junpeng Zhu,Jian Zhang
出处
期刊:DEStech Transactions on Computer Science and Engineering
[DEStech Publications]
日期:2018-03-27
卷期号: (cmee)
被引量:1
标识
DOI:10.12783/dtcse/cmee2017/19992
摘要
Satellite orbit prediction is a basic requirement in satellite applications. The current orbit prediction mainly depends on the dynamic model. Because of the limitations of the detection equipment and the satellite orbit data cannot be updated in time, which cause the dynamical model long-term orbit divergence to be serious. Using deep neural network as a method of orbit prediction which can predict the future data by training the satellite orbit data and grasp the implicit relationship between the data. The neural network model is optimized and the prediction data is compared with the actual data. The error of 20 days forecast is reduced to 2km, which improves the accuracy of neural network forecasting satellite orbit.
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